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Our ambition is to serve different types of audiences. The first set of users consists of practitioners in the natural and life sciences, such as in bioinformatics, sensometrics, chemometrics, statistics, and machine learning. They will mainly be interested in the question how to perform multiblock data analysis and what to use in which data analysis situation. They may benefit from reading the main text and studying the examples. The second set of users are method developers. They want to know what is already available and spot niches for further development; apart from the main text and the examples they may also be interested in the elaborations. The final set of users are computer scientists and software developers. They want to know which methods are worthwhile to build software for and may also study the algorithms.

We will try to serve all groups. This means that we will explain most of the methods in a rather detailed manner (especially in Parts II and III) and will also pay attention to validation and visualisation to encourage proper interpretation. At the end of the book in Chapter 11, we describe multiblock toolboxes and packages in R, MATLAB and Python and showcase the accompanying R package multiblock which includes many of the methods described in this book.